Animal Behavior Prediction with Long Short-Term Memory
Henry Roberts, Aviv Segev · 2020
A foundational step in the study of any animal is the establishment of an accurate behavioral model. Building a model that is capable of defining and predicting an animal's behavior is critical to advancing ethological theory and research, however many animal models fail to be sufficiently thorough or often do not exist at all. Great pools of data are available for improving these models through recorded video of animals posted on video hosting sites throughout the internet, however these sources are largely left unused due to their sheer quantity being too much for researchers to manually observe and annotate. This paper proposes a method for efficiently converting video of animals at any length into models capable of making accurate behavioral prediction. This predictive model is developed through a data processing pipeline merging an ensemble meta-algorithm for behavior classification with a long short-term memory network for temporal pattern recognition and prediction. The application of this pipeline produced results with a higher degree of predictive accuracy compared to more traditional autoregressive techniques. These findings suggest the method has significant potential as a tool for efficiently developing new models and findings in the study of animal behavior. The method's performance in prediction also suggests that it may have further application in building models for the prediction of events or failures in inorganic, semi-stochastic processes such as video monitored mechanical systems and equipment.